Security Of Cloud Services Research Articles

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

SummaryIndustrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods.

Editorial: Security of cloud service for the manufacturing industry

With the rapid development of the industrial Internet, cloud service-based manufacturing has emerged as a next generation manufacturing paradigm that has potential to revolutionize the manufacturing industry. It is foreseeable that cloud services will be popular in the next generation manufacturing industry. In recent years, more and more manufacturing companies have recognized the benefits of cloud service and have developed cloud-based manufacturing models. However, the security problems need to be researched and solved for the cloud services in the manufacturing industry, especially the data security issues are important, and restrict the cloud application in the manufacturing industry. Regarding the security of data, some people believe that when data are stored in the cloud, manufacturing companies lose control of the data. The manufacturing companies focus on how to secure the data from the top-level management and how to minimize the security risks, such as those caused by data security or service migration challenges. The manufacturing companies select the reliable cloud service while consider function and budget feasibility. How to ensure security in cloud service for the manufacturing industry has become a topic of increasing interest for both academic researchers and developers from the industry. This special issue addresses this emerging and fast developing research area on cloud security in manufacturing industry. The summary of these papers is as follows. [...]

Blockchain based cloud service security architecture with distributed machine learning for smart device traffic record transaction

AbstractIn today's world, the transaction through the smart device has received greater attention and configures numerous applications which can efficiently process huge traffic records on a growing demand for service centers from the edge of the networks. Due to these immense growths, the concern raises in critical transaction records in terms of system security threats and efficiency issues in the smart devices. However, existing methods failed due to security attacks during the tenure of access transactions and aggregated services. Recently, blockchain technology enables service centers depends on various platforms to share transaction records. But, it is difficult to store the transaction record because of its size. To address these issues, we proposed a SECure LearningChain (SEC‐LearningChain) design based on the integration of blockchain technology, machine learning (ML), and cloud computing primitives are applied together for a secure data transaction in a Peer to Peer network as well as efficient data sharing service. This approach consists of four design models: First, an attack detection model detects the attack using threshold‐based anomalous traffic detector in the transaction network. Second, a mold blockchain transaction network model is designed based on the cryptographic hash and encryption to deal with threats and validate the identity verification process for a secure transaction. Next, the large‐scale transaction record is optimized and trains the ML model for the output prediction. Finally, the cloud assessment model manages the stored transaction records and easily share the accessed services across different cloud platforms for each service center. Furthermore, we prove that the SEC‐LearningChain design resists transmission control protocol flooding attack, denial of service attack, and falsify attack. Experimental results demonstrate that the performance of the SEC‐LearningChain achieves more number of transactions in each blocks over existing schemes.

An archetype for mitigating the security threats in multi-cloud environment by implementing tree-based next-generation firewalls

Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases.

An assessment model for cloud service security risk based on entropy and support vector machine

SummaryCloud services are open, shared, and complex. These characteristics will lead to many security risks and will affect the development of cloud services. Therefore, it is necessary to identify and measure the security risks of cloud services. However, there are still many deficiencies in this area. In light of this, this paper carries out an in‐depth study from the perspective of technical security risk. Firstly, this paper combines the three aspects on cloud service security problems, objectives, and technologies. It attempts to explore the technological solutions to get security risk problems to achieve the expected security goals, and establish the cloud service technology security risk index system. Secondly, because of the strong subjectivity and the deficiency of the data obtained from cloud service providers, this paper establishes a cloud service security risk assessment model based on entropy weight theory and multi‐classification support vector machine. Finally, the experimental results show that the evaluation model is feasible and effective.

AN ANALYTICAL STUDY ON CLOUD COMPUTING

This paper is about the An Analytical Study on Cloud Computing. Cloud computing is the development of parallel computing, grid computing, distributed computing, and virtualization technologies which define the shape of a new area. In contrast to a variety of benefits related with cloud computing, there are certain challenges as well. These challenges include security, privacy and reliability of data, high costs of data transfers, and regularity in the availability of services, and bugs in large-scale distributed systems. Cloud computing is a method of computing in a place that provides users with the ability of information technology as a service and allows them to have access to these services on the Internet without having limited information. Cloud computing can help businesses transform their closing server infrastructures into dynamic environments, expanding and reducing server capacity depending on their demand. Cloud is a image to describe web as a space where computing has been pre-installed and close as a service; data, operating systems, applications, storage and processing power close on the web ready to be shared. In cloud computing data owners expand their complex data management systems from community sites to the public cloud for great pliability and cost-effective. The searching of this study that highlight there are five main issues related with cloud computing execution which are Mobility and Cloud Government Application security issues, Cloud Security data, cloud security platform, Cloud Security Services and Application, cloud network security issues and infrastructure issues.

Efficiency of Semantic Web Implementation on Cloud Computing: A Review

Semantic web and cloud technology systems have been critical components in creating and deploying applications in various fields. Although they are self-contained, they can be combined in various ways to create solutions, which has recently been discussed in depth. We have shown a dramatic increase in new cloud providers, applications, facilities, management systems, data, and so on in recent years, reaching a level of complexity that indicates the need for new technology to address such tremendous, shared, and heterogeneous services and resources. As a result, issues with portability, interoperability, security, selection, negotiation, discovery, and definition of cloud services and resources may arise. Semantic Technologies, which has enormous potential for cloud computing, is a vital way of re-examining these issues. This paper explores and examines the role of Semantic-Web Technology in the Cloud from a variety of sources. In addition, a "cloud-driven" mode of interaction illustrates how we can construct the semantic web and provide automated semantical annotations to web applications on a large scale by leveraging Cloud computing properties and advantages.

Development of Multisource Cloud Logs Forensic with encase Tool for performing Lock Technique using Deep Analysis and Reporting

Encase and FTK tool will be utilised too much in Cyber security. In view of this Cloud forensic is an research which would be binded together by cloud computing and cyber security. Cyber forensic is connected to cloud computing and different services of CC such as Infrastructure-As-A-Service (IaaS), Platform-As-A-Service (PaaS),Software-As-A-Service(SaaS) ,Data-StorageAs-A-Service (DSaaS) will be applied in different domains.. cloud forensic techniques will be used if any attacks on software, platforms, infrastructure, and storage must be investigate, report and recovered data . Many cloud forensic techniques has been innovated and proposed in literature and tools have been using in IT industries to provide secure cloud services. With this evaluation of techniques and tools, attackers evolve their anti-forensic techniques as well to break the security measures. Hence the continuous monitoring and research care is indeed required in cyber security and forensic fields.. Hence log recovery and forensic have been given highest priority on cloud resources and functioning towards improving the cloud security and cloud forensic systems.

Implementing Hybrid Security Mechanism for Cloud Considering Intrusion, Sql Injection and Performance Degradation

Considering the demand of cloud services research has considered the issues or problems related to cloud computing. Various approaches adopted by existing research have limited scope and there is need to increase the security of cloud computing environment. The issues of security threat in cloud environment are explained in this paper. There have been several security threats to cloud environment such as Intrusion, brute force, Sql injection, Trozen horse that could affect the security of cloud services. There remains issue of Un-authentic access. Moreover the identity management is becoming a great challenge. Previous researches have proposed cryptographic approach while some provided solution to hacking attempts along with unauthentic external access but these security mechanisms are not sufficient to protect the cloud. Research paper is introducing intelligent system that is capable to trace the intrusion using LSTM based training model. The model is trained in order to categorize intrusion accordingly. The focus of research is to increase the security from intrusion by providing intelligent LSTM approach. This mechanism would classify the transmission in different categories such as Dos-synflooding, MITM ARP spoofing, Mirai- Ackflooding, Mirai-Http flooding, Mirai-Hostbrute forceg, Mirai-UDP Flooding, scan hostport and Normal. Moreover research paper has focused on prevention of Sql injection attacks. In order to increase the security between sender and receiver research has also allowed two way port based hand shaking in order to transmit data more securely. The transmission would be initiated using default port but the actual transmission would be made using random port that would be set for specific time slot.

A Review of Cloud Service Security with Various Access Control Methods

A Review of Cloud Service Security with Various Access Control Methods

Distributed Functional Signature with Function Privacy and Its Application

We introduce a novel notion of distributed functional signature. In such a signature scheme, the signing key for function f will be split into n shares sk f i and distributed to different parties. Given a message m and a share sk f i , one can compute locally and obtain a pair signature f i m , σ i . When given all of the signature pairs, everyone can recover the actual value f m and corresponding signature σ . When the number signature pairs are not enough, nobody can recover the signature f m , σ . We formalize the notion of function privacy in this new model which is not possible for the standard functional signature and give a construction from standard functional signature and function secret sharing based on one-way function and learning with error assumption. We then consider the problem of hosting services in multiple untrusted clouds, in which the verifiability and program privacy are considered. The verifiability requires that the returned results from the cloud can be checked. The program privacy requires that the evaluation procedure does not reveal the program for the untrusted cloud. We give a verifiable distributed secure cloud service scheme from distributed functional signature and prove the securities which include untrusted cloud security (program privacy and verifiability) and untrusted client security.

A secure cloud service deployment framework for DevOps

<span>The advancements in cloud computing and leveraging the benefits from cloud computing to the service providers have increased the deployment of traditional applications to the cloud. The applications once deployed on the cloud, due to various reasons, need migration from development infrastructure to operational infrastructure, one operational instance to other operational instances due to load balancing and the cycle continues due to the use of DevOps as development strategies for cloud computing applications. Advocates of hybrid and public clouds observe cloud computing makes it possible for organizations to avert or minimize upfront IT infrastructure expenses. Proponents also assert that cloud computing systems permit businesses to receive their software up and running faster, using improved manageability and less maintenance, so it empowers IT teams to rapidly adapt tools to meet the varying and unpredictable requirements. DevOps is a lot of practices that mechanizes the procedures between programming improvement and IT groups, all together that they can fabricate, test, and discharge programming quicker and even more dependably. The idea of DevOps is established on building a culture of a joint effort between groups that generally worked in relative siloes. The guaranteed advantages incorporate expanded trust, quicker programming discharges, capacity to explain basic issues rapidly and better oversee impromptu work. Thus, this work identifies the need for providing multiple security protocols during the complete life cycle of cloud application development and deployment. This work proposes a novel framework for automatic selection and deployment of the security protocols during cloud service deployments. The framework identifies the need for security aspects and selects the appropriate security algorithms for virtual machines. The proposed framework demonstrates nearly 80% improvement over the security policy deployment time. </span>

A Hybrid Encryption Solution to Improve Cloud Computing Security using Symmetric and Asymmetric Cryptography Algorithms

Ensuring the security of cloud computing is one of the most critical challenges facing the cloud services sector. Dealing with data in a cloud environment, which uses shared resources, and providing reliable and secure cloud services, requires a robust encryption solution with no or low negative impact on performance. Thus, this study proposes an effective cryptography solution to improve security in cloud computing with a very low impact on performance. A complex cryptography algorithm is not helpful in cloud computing as computing speed is essential in this environment. Therefore, this solution uses an improved Blowfish algorithm in combination with an elliptic-curve-based algorithm. Blowfish will encrypt the data, and the elliptic curve algorithm will encrypt its key, which will increase security and performance. Moreover, a digital signature technique is used to ensure data integrity. The solution is evaluated, and the results show improvements in throughput, execution time, and memory consumption parameters.

Runtime Randomized Relocation of Crypto Libraries for Mitigating Cache Attacks

Crypto libraries such as OpenSSL and Libgcrypt are essential building blocks for implementing secure cloud services. Unfortunately, these libraries are subject to cache side-channel attacks, which are more devastating in cloud environments where inevitable cache contention among different tenants occurs. Previous approaches for mitigating cache side-channel attacks have limitations in terms of the deployability and security; these hinder utilization in cloud services. In this paper, we propose an R2-relocator, a novel library protection technique based on moving target defence. When injected into a running process, the R2-relocator performs randomized relocation of the library during runtime. By doing this, it transforms a vulnerable crypto library into one that randomly changes its memory (cache) location, thereby preventing the delivery of cache side-channel attacks against the library. The proposed technique achieves robust protection against cache side-channel attacks for all crypto libraries, even those containing unpatched critical vulnerabilities, without the need for reconfiguration of the library. Extensive evaluations of security, performance, and deployability of the R2-relocator demonstrate its effectiveness for secure cloud services.

Recurrent Neural Networks Based Online Behavioural Malware Detection Techniques for Cloud Infrastructure

Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key cloud service providers (CSPs) like Amazon, Microsoft and Google offer Infrastructure as a Service (IaaS) to meet the growing demand of such enterprises. This increased utilization of cloud platforms has made it an attractive target to the attackers, thereby, making the security of cloud services a top priority for CSPs. In this respect, malware has been recognized as one of the most dangerous and destructive threats to cloud infrastructure (IaaS). In this paper, we study the effectiveness of Recurrent Neural Networks (RNNs) based deep learning techniques for detecting malware in cloud Virtual Machines (VMs). We focus on two major RNN architectures: Long Short Term Memory RNNs (LSTMs) and Bidirectional RNNs (BIDIs). These models learn the behavior of malware over time based on run-time fine-grained processes system features such as CPU, memory, and disk utilization. We evaluate our approach on a dataset of 40,680 malicious and benign samples. The process level features were collected using real malware running in an open online cloud environment with no restrictions, which is important to emulate practical cloud provider settings and also capture the true behaviour of stealth and sophisticated malware. Both our LSTM and BIDI models achieve high detection rates over 99% for different evaluation metrics. In addition, an analysis study is conducted to understand the significance of input data representations. Our results suggest that in particular cases, input ordering does have some affect on the performance of the trained RNN models.

Access Point Authentication Scheme of SCADA System Based on Cloud Computing Technology

Aiming at the security problem of the SCADA system introduced into cloud computing that lacks a credible identity authentication mechanism, this paper proposes a cloud computing-based SCADA system access point authentication scheme. In this scheme, a cloud service security access protocol for SCADA system is proposed. The protocol uses password based identity authentication technology to screen the identity of the terminal node, and effectively identifies the illegal access point by authenticating the access point. By this method, the user’s private information is prevented from being intercepted and tampered maliciously.

CSPM

Security and privacy in cloud systems are critical. To address security and privacy concerns, many security patterns, privacy patterns, and non-pattern-based knowledge have been reported. However, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the cloud security and privacy metamodel (CSPM). CSPM uses a consistent approach to classify and handle existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

Data Storage Security in the Cloud Environment Using the Honey Pot System

Cloud computing is a framework for delivering services through a shared network. It should follow certain procedures or mechanisms to protect and make the cloud data more reliable. In secure cloud computing, the implementation and service delivery model are dealt with. Cloud security could be viewed as a provider and customer responsibility depending on their design and level. There are cases where multiple cloud users can use the same information to create service abnormalities, which is called ‘multitenancy.' In a multi-tenant model, the resources are isolated between different users and it is done by segmentation to achieve the goal of data security between many users. In the user access area, user access to common resources is also limited. The software is aware of the actions of the client and acts accordingly if unauthorized access is detected. The authors propose a clear idea of the data storage security of cloud information through honeypot in cloud monitoring applications and various cloud security scheme key models used and also identifies the audit time and invalid response time.

DIAGNOSING AND COUNTERACTING NETWORK THREATS

Information security in cloud services is one of the most important issues that has attracted much research and development over the last few years. In particular, attackers can investigate the vulnerabilities of the cloud system and compromise virtual machines to deploy further large-scale distributed malware (DDoS). DDoS attacks typically include early-stage actions such as multi-stage exploitation, low-frequency vulnerability scanning, and compromising of identified vulnerable virtual machines as subordinate, and finally DDoS attacks through compromised virtual machines. In a cloud system, especially infrastructure-as-a-service (IaaS) clouds, detecting attacks from such slave machines is extremely difficult. This is because cloud users can install vulnerable programs on their virtual machines. To avoid the compromise of vulnerable virtual machines in the cloud, a multiphase mechanism for detecting, measuring and selecting a countermeasure based on analytical models based on well-established countermeasures based on a virtual network is proposed. System and safety assessments demonstrate the effectiveness and efficiency of the proposed solution.

Joint Pricing and Security Investment in Cloud Security Service Market With User Interdependency

After several decades of development on cyber security techniques, one clear conclusion can be drawn: no cyber security solution can completely remove the risks faced by the users. In this regard, cyber-insurance has been introduced as a means to enable the users to alleviate the damage from the cyber threats by transferring the cyber risks to an insurer. In this article, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To lower the successful attack probability, the CSSV has an incentive to invest in improving its cloud security service. Specifically, we model and study the cloud security service market in the framework of a two-stage Stackelberg game. On the upper stage, the CSSVs lead to decide on their own strategies, i.e., the price of the cloud security plan and the security investment to improve their offered cloud security service. On the lower stage, the users follow to decide on the purchase of the cloud security plan according to the price of the cloud security plan and the perceived cyber breach probability of the cloud security service. We analytically verify that the Stackelberg equilibrium exists and is unique. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game. The performance evaluation shows some insightful results. For example, when the users have strong interdependency, the profits of the CSSVs become lower.